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Using machine learning to automatically measure axial vertebral rotation on radiographs in adolescents with idiopathic scoliosis

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MEDICAL ENGINEERING & PHYSICS 2022年 107卷 103848-103848页

作者： Logithasan, Veena Wong, Jason Reformat, Marek Lou, Edmond Univ Alberta Dept Elect & Comp Engn Edmonton AB T6G 1H9 Canada 11-263 Donadeo ICE Bldg 9211-116 St Edmonton AB T6G 1H9 Canada

Adolescent idiopathic scoliosis is a 3D lateral spinal curvature coupled with axial vertebral rotation (AVR). Measuring AVR during clinic is important because it affects treatment options and predicts the risk of scoliosis progression. However, manual measurements are time consuming and have high inter-rater and intra-rater errors. This study aimed to develop a machine learning algorithm based on convolutional neural networks (CNNs) to automatically calculate AVR on posteroanterior radiographs using three different segmentations including spinal column, individual vertebra, and pedicles. Separate labeling and training processes were performed on each of the developed segmentation algorithms. The final machine learning software was tested on 221 vertebrae from 17 spinal radiographs. An experienced rater with over 25 years of experience measured the 221 vertebral rotations manually. By comparing the manual and the fully automatic measurements, 81% (178/221) of the automatic measurements were within the clinical acceptance error (+5 degrees). The mean absolute difference and the standard deviation between the manual and automatic measurements was 4.3 degrees +/- 5.7 degrees. Based on the Bland-Altman plot, the manual and automatic measurements had a strong correlation and no bias. The error did not relate to the severity of the rotation. This method is fully automatic, and the result is comparable to others.

关键词： Adolescent idiopathic scoliosis Vertebral rotation machine learning algorithm Automatic measurements

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Trigonometric-Euclidean-Smoother Interpolator (TESI) for continuous time-series and non-time-series data augmentation for deep neural network applications in agriculture

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COMPUTERS AND ELECTRONICS IN AGRICULTURE 2023年第1期206卷

作者： Derraz, Radhwane Muharam, Farrah Melissa Jaafar, Noraini Ahmad Yap, Ng Keng Putra Malaysia Univ Dept Agr Technol Bangi 43400 Selangor Malaysia Putra Malaysia Univ Dept Land Management Bangi 43400 Selangor Malaysia Putra Malaysia Univ Inst Math Res Bangi 43400 Selangor Malaysia

Biomass estimation, fertilisation, and crop production reflect crop yield potential. The prediction of these var-iables allows the selection of crop cultivars with high yield potential. Deep neural networks (DNNs) can predict such crop variables. However, DNNs are data greedy algorithms that overfit/underfit on small-size datasets. Additionally, the collection of big data is expensive and laborious. Therefore, providing synthetic big data is preferable. This study aims to: (i) develop a trigonometric-Euclidean-smoother interpolation (TESI) for contin-uous time-series and non-time-series data augmentation to prevent DNNs from under/overfitting;(ii) compare the TESI performance to the tabular variational autoencoder (TVAE) and the conditional tabular generative adversarial network (CTGAN);and (iii) compare the DNN performance before and after data augmentation. Two time-series datasets, oil palm production and rice production, and two non-time-series datasets, fertiliser and rice total aboveground biomass (TAGB), were augmented using the TESI, TVAE, and CTGAN algorithms. The TESI retained the features' original probability distribution in the four datasets. The C-TESI achieved the lowest mean squared error mean percentage (MAEP) on the oil palm (0.60-2.85%), rice (0.77-1.72%), and fertiliser datasets (2.04-2.21%). The TESI retained the variance inflation factor (VIF) ranges less than 10 on the four datasets;the TESI retained a VIF range of 1.99-10.06 or reduced the VIF range to 1.55-6.66. Furthermore, the TESI retained the Spearman's r (rs) range of 0.79-0.97 or increased it to 0.81-0.99 on the four datasets. The DNN achieved the highest coefficient of determination (R2) (0.77-0.99) and lowest root mean squared error (RMSE) ranges (2.8E+01-8.1E+05) on the four datasets augmented with the TESI. The Q-TESI, C-TESI, and L-TESI overcame the LN-TESI in retaining the features' original probability distribution, minimising the augmentation loss, reducing the VIF, increasing

关键词： Continuous data augmentation Deep neural network Autoencoder Smart farming machine learning algorithm Generative adversarial network

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Hardware Trojans Detection through RTL Features Extraction and machine learning 6

Hardware Trojans Detection through RTL Features Extraction a...

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IEEE Asian Hardware Oriented Security and Trust Symposium (AsianHOST)

作者： Yang, Jizhong Zhang, Ying Hua, Yifeng Yao, Jiaqi Mao, Zhiming Chen, Xin Nanjing Univ Aeronaut & Astronaut Coll Elect & Informat Engn Nanjing Peoples R China

ISBN: (纸本)9781665441858

To ensure the hardware security of integrated circuits, especially considering third-party IP cores (3PIP cores) application in SoC design, a novel Hardware Trojan (HT) detection scheme aimed to Register Transfer Level (RTL) Description is proposed in this paper. By analyzing the structural and signal characteristics of RTL design for suspicious circuits, a mathematical model of RTL nodes is constructed to achieve numeric features relevant to HT and available for machine learning (ML). Then a ML classifier for a certain category of circuits is trained by Random Forest algorithm and numerical features are extracted. 22 circuits are applied to training and 22 circuits to detecting in experiments, the results show that the average HT detection rate of our proposed detection method can reach 99.93%.

关键词： hardware trojans feature extraction register transfer level machine learning algorithm

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machine learning role in clinical decision-making: Neuro-rehabilitation video game

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EXPERT SYSTEMS WITH APPLICATIONS 2022年 201卷

作者： Esfahlani, Shabnam Sadeghi Shirvani, Hassan Butt, Javaid Mirzaee, Iraj Esfahlani, Karim Sadeghi Anglia Ruskin Univ Engn & Built Environm Bishop Hall Lane Cambridge CM1 1SQ Essex England Urmia Univ Technol Orumiyeh Iran

In this study, we investigated the potential use of machine learning algorithms (ML) to predict the outcome of home-based neuro-rehabilitation video game intervention and its advantage in supporting clinical decision-making. We adopted Support Vector machines (SVM) and K-Nearest Neighbors (KNN) to develop multidimensional functions (multi-variable Kernel functions) since both algorithms were considered significant and active analysis agents for prediction and classification. Supervised SVM and KNN algorithms were trained using the upper extremity (arm, forearm, and hand) joints' kinematic data and hand gestures of participants while interacting with the developed video games. Data collected from healthy and Multiple sclerosis (MS) participants were compared and used to develop the predictive algorithm. Pre-and post-rehabilitation data of MS subjects were investigated and used to assess the subject's functional improvements following the program. Bayesian optimization, Sigmoid, polynomial, and Gaussian Radial Basis functions were utilized for training and predicting outcomes. The results showed that the first two kernel regressions had the best performance regarding predictability and cross-validation loss. KNN's prediction accuracy was exceeded by 91.7% versus SVM, which was 88.0%. The effectiveness of the rehabilitation program was assessed through Spatiotemporal control and motor assessment scale presenting 40% improvement. Our findings suggest that ML has a great potential to be used for decision-making in neuro-rehabilitation programs.

关键词： machine learning algorithm Rehabilitation Serious Video Game Support Vector machine K-Nearest Neighbors

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Analysis of Trust in Ukrainian banks based on machine learning algorithms 9

Analysis of Trust in Ukrainian banks based on Machine Learni...

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9th International Conference on Advanced Computer Information Technologies (ACIT)

作者： Adamyk, Bogdan Skirka, Andriy Snihur, Khrystyna Adamyk, Oksana Ternopil Natl Econ Univ Dept Banking Ternopol Ukraine Management Ternopil Natl Econ Univ Ivano Frankivsk Educ & Res Inst Ivano Frankivsk Ukraine Ternopil Natl Econ Univ Dept Econ Evaluat & Business Audit Ternopol Ukraine

ISBN: (纸本)9781728104492

Propose: This paper aims to conduct a thorough analysis to determine the influence of several possible factors on the level of confidence in Ukrainian banks. This scientific research project is based on data from the World Values Survey (WVS). The paper also aims to empirically investigate a number of independent variables, that creates trust in banks, based on machine learning algorithms. Besides that, use some of the predictive analytics techniques to anticipate the level of trust in Ukrainian banks. Methodology: to calculate the index of Confidence in Banks var., by using linear regression, logistic regression (as a robustness check), Random Forrest, Decision Tree and XGBoost Models. Verify the output and results by using the Residual graphs, Cross-validation, Confusion matrix, ROC and model accuracy estimations. Main Findings: age, level of financial satisfaction, scale income and life satisfaction, general trust, lack of cash and other indicators has a significant impact on the the level of trust in Ukrainian banks. This paper represents a number and probability value of the variables spectrum;effect plots and other visualization graphs.

关键词： trust in banks data science machine learning algorithm data visualization deposit guarantee bank run

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Extreme gradient boosting (Xgboost) model to predict the groundwater levels in Selangor Malaysia

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AIN SHAMS ENGINEERING JOURNAL 2021年第2期12卷 1545-1556页

作者： Osman, Ahmedbahaaaldin Ibrahem Ahmed Ahmed, Ali Najah Chow, Ming Fai Huang, Yuk Feng El-Shafie, Ahmed Univ Tenaga Nas Dept Civil Engn Coll Engn Kajang 43000 Selangor Darul Malaysia Univ Tenaga Nas Inst Energy Infrastruct IEI Kajang 43000 Selangor Malaysia Univ Tenaga Nas Inst Sustainable Energy ISE Kajang 43000 Selangor Malaysia Univ Tunku Abdul Rahman Lee Kong Chian Fac Engn & Sci Dept Civil Engn Kajang Selangor Malaysia Univ Malaya UM Fac Engn Dept Civil Engn Kuala Lumpur 50603 Malaysia United Arab Emirates Univ Natl Water & Energy Ctr NWC POB 15551 Al Ain U Arab Emirates

Groundwater levels have been declining recently in Malaysia. This is why, the current study was aimed to propose an accurate groundwater levels prediction model using machine learning algorithms in highly populated towns in Selangor, Malaysia. The models developed used 11 months of previously recorded data of rainfall, temperature and evaporation to predict groundwater levels. Three machine learning models have been tested and evaluated;Xgboost, Artificial Neural Network, and Support Vector Regression. The results showed that for the first scenario, which had combinations of 1,2 and 3 days delayed of rainfall data only considered as an input, the models' performance was the worst. while in the second scenario the proposed Xgboost model outperformed both the Artificial Neural Network and Support Vector Regression models for all different input combinations. A significant increase in performance was achieved in the third scenario, when using 1 day delayed of groundwater levels as an input as well where R-2 equal to 0.92 in the Xgboost model in scenario 3 and 0.16, 0.11 in scenarios 2 and 1 respectively. The results obtained in this study serves as a great benchmark for future groundwater levels prediction using Xgboost algorithm. (C) 2020 The Authors. Published by Elsevier B.V. on behalf of Faculty of Engineering, Ain Shams University.

关键词： Groundwater level prediction machine learning algorithm Artificial neural network Support vector regression Cross-correlation

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Classification of Pharynx from MRI Using a Visual Analysis Tool to Study Obstructive Sleep Apnea

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CURRENT MEDICAL IMAGING 2021年第5期17卷 613-622页

作者： Shahid, Muhammad Laiq Ur Rahman Mir, Junaid Shaukat, Furqan Saleem, Muhammad Khurram Tariq, Muhammad Atiq Ur Rehman Nouman, Ahmed Jacobs Univ Dept Comp Sci & Elect Engn Bremen Germany Univ Surrey Ctr Vis Speech & Signal Proc Guildford Surrey England Univ Sheffield Dept Elect & Elect Engn Sheffield S Yorkshire England Koc Univ Dept Mech Engn Istanbul Turkey Victoria Univ Coll Engn & Sci Melbourne Vic Australia Sabanci Univ Dept Mechatron Engn Istanbul Turkey

Background: Obstructive sleep apnea (OSA) is a chronic sleeping disorder. The analysis of the pharynx and its surrounding tissues can play a vital role in understanding the pathogenesis of OSA. Classification of the pharynx is a crucial step in the analysis of OSA. Methods: A visual analysis-based classifier is developed to classify the pharynx from MRI datasets. The classification pipeline consists of different stages, including pre-processing to select the initial candidates, extraction of categorical and numerical features to form a multidimensional features space, and a supervised classifier trained by using visual analytics and silhouette coefficient to classify the pharynx. Results: The pharynx is classified automatically and gives an approximately 86% Jaccard coefficient by evaluating the classifier on different MRI datasets. The expert's knowledge can be utilized to select the optimal features and their corresponding weights during the training phase of the classifier. Conclusion: The proposed classifier is accurate and more efficient in terms of computational cost. It provides additional insight to better understand the influence of different features individually and collectively. It finds its applications in epidemiological studies where large datasets need to be analyzed.

关键词： machine learning algorithm medical image analysis classification MRI visual analysis multidimensional feature space OSA

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Torque Performance Improvement of Permanent Magnet Arc Motor Based on Two-Step Strategy

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IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS 2021年第11期17卷 7523-7534页

作者： Pan, Zhenbao Fang, Shuhua Southeast Univ Sch Elect Engn Nanjing 210096 Peoples R China

In this article, we present a two-step strategy for the torque performance improvement of a dual-stator permanent magnet arc motor (DS-PMAM), which is used in some industrial applications, such as robot joint. The proposed strategy can achieve high average torque, low torque ripple, and, eventually, improve the quality of manufactured products. In the first step, the no-load torque model caused by the end effect is analytically established. In order to suppress the inductance unbalance and the torque ripple, two DS-PMAM models with different winding connection methods are investigated and compared in terms of inductances, backelectromotive force, and torque characteristics. In the second step, a new sensitivity analysis method based on the shapley additive explanations value is first proposed to evaluate the sensitivity of each structural parameter to different optimization objectives. Then, an efficient optimization design method is proposed by combining the machine learning algorithm named eXtreme gradient boosting (XGBoost) and the intelligent optimization algorithm called nondominated sorting genetic algorithm-II (NSGA-II). The XGBoost is innovatively introduced to efficiently approximate the function relationship between the optimization objectives and the structural parameters. The NSGA-II is adopted to determine the optimal combination of the structural parameters and motor performances. Finally, based on the optimal results obtained by the two-step strategy, a prototype of DS-PMAM is manufactured and tested to validate the proposed strategy.

关键词： Torque Stators Permanent magnet motors Optimization Windings Torque measurement Rotors EXtreme gradient boosting (XGBoost) machine learning algorithm nondominated sorting genetic algorithm-II (NSGA-II) permanent magnet (PM) motor shapley additive exPlanations (SHAP) torque

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LightGBM Technique and Differential Evolution algorithm-Based Multi-Objective Optimization Design of DS-APMM

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IEEE TRANSACTIONS ON ENERGY CONVERSION 2021年第1期36卷 441-455页

作者： Pan, Zhenbao Fang, Shuhua Wang, Hui Southeast Univ Sch Elect Engn Nanjing 210096 Peoples R China Southeast Univ Sch Instrument Sci & Engn Nanjing 210096 Peoples R China

This article proposes a multi-objective optimization method for the optimization design of a new dual-stator arc permanent magnet machine (DS-APMM) which can be applied on the direct-drive scanning systems with limited angular movement, such as radar, large telescope. The proposed optimization method integrates light gradient boosting machine (LightGBM) with differential evolution algorithm (DEA) to achieve optimal design objectives of high back electromotive force, low total harmonic distortion, high average torque, and low torque ripple at different rotor speeds. The machine topology and analytical model of DS-APMM are firstly presented to determine the structural parameters to be optimized. The sensitivity of each structural parameter to the optimization objectives is analyzed based on the SHAP (SHapley Additive exPlanations) value. Then, a finite-element analysis (FEA)-based DS-APMM model is developed to acquire sample data. Based on the acquired sample data, a machine learning algorithm, LightGBM, is introduced to establish surrogate model that can fit the function relationship between design objectives and structural parameters. Subsequently, an intelligent search algorithm named DEA is adopted to search optimal combination of the structural parameters and hence obtain optimal machine performances of DS-APMM. Finally, the electromagnetic characteristics of the initial model, middle model and optimal model of DS-APMM are compared and analyzed, both FEA and prototype experiments verify the feasibility and superiority of the proposed multi-objective optimization method.

关键词： Optimization Analytical models Structural engineering Stator windings Rotors Data models Analytical model differential evolution algorithm (DEA) finite-element analysis (FEA) light gradient boosting machine (LightGBM) multi-objective optimization machine learning algorithm permanent magnet machine sensitivity analysis surrogate model SHAP value

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EfficientMaize: A Lightweight Dataset for Maize Classification on Resource-Constrained Devices

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DATA IN BRIEF 2024年 54卷 110261页

作者： Asante, Emmanuel Appiah, Obed Appiahene, Peter Adu, Kwabena Univ Energy & Nat Resources Dept Comp Sci & Informat Sunyani Ghana

Hyperspectral imaging, combined with deep learning techniques, has been employed to classify maize. However, the implementation of these automated methods often requires substantial processing and computing resources, presenting a significant challenge for deployment on embedded devices due to high GPU power consumption. Access to Ghanaian local maize data for such classification tasks is also extremely difficult in Ghana. To address these challenges, this research aims to create a simple dataset comprising three distinct types of local maize seeds in Ghana. The goal is to facilitate the development of an efficient maize classification tool that minimizes computational costs and reduces human involvement in the process of grading seeds for marketing and production. The dataset is presented in two parts: raw images, consisting of 4,846 images, are categorized into bad and good. Specifically, 2,211 images belong to the bad class, while 2,635 belong to the good class. Augmented images consist of 28,910 images, with 13,250 representing bad data and 15,660 representing good data. All images have been validated by experts from Heritage Seeds Ghana and are freely available for use within the research community. (c) 2024 The Author(s). Published by Elsevier Inc. This is an open access article under the CC BY license ( http://***/licenses/by/4.0/ )

关键词： machine learning Maize dataset Image recognition machine learning algorithm Precision agriculture Convolutional Neural Network

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